This paper aims to empirically compare the performance of the smooth transition exponential smoothing (STES) method against the well-known generalized autoregressive conditional heteroskedasticity (GARCH) model in one-step-ahead volatility forecasting. While the GARCH model captured most of the stylized facts of the financial time series, threats of outliers in the leptokurtic distributed series remain unresolved. The study compared volatility forecasting performance of a total of 22 models and methods comprising STES, GARCH, and some ad-hoc forecasting. The daily returns of seven mutual fund indices (derived from 57 individual equity mutual funds) under two different economic conditions (sub-periods) were applied across all competing models. Findings revealed that the STES method with error and absolute error as transition variables emerged as the best post-sample volatility forecasting model in both sub-periods with and without financial crisis impact, as verified by model confidence set (MCS) procedure. The implications based on the results are: (1) both the sign and size of yesterday’s news shock have an impact on today’s volatility; (2) the STES method is resilient to outliers, and hence superior to GARCH and other volatility forecasting approaches examined. This study contributes an empirical approach in forecasting the risk of mutual funds investment for investors and fund managers, as well as extending the scope of volatility forecasting literature into the less explored mutual funds.

Even though both symmetric and asymmetric conceptions of news impacts are well-established in the disciplines of economics and financial markets, the effects of combining multiple news shocks on the volatility of tourism demand have not yet been delved into or gauged in any tourist destination. This work hypothesises and verifies that the news impact curve (NIC), conditional heteroscedastic volatility models, and multiple news shocks are suitable for forecasting the volatility of the Malaysian tourist industry. Among them, three primarily volatility models (GARCH, EGARCH, and GJRGARCH) are used in conjunction with five financial news shocks (FFNSs), namely the Kuala Lumpur Composite Index (KLCI), the United States Dollar Index (DXY), the stock performance of 500 large companies listed on stock exchanges (S&P500), Crude Oil (CO), and Gold Price (GP). Among the most significant findings of this study are the demonstration of monthly seasonality using conditional mean equations, asymmetry effects in EGARCH-FFNSs, and GJRGARCH-FFNSs models in conditional variance equations and 50 NICs, and the GARCH-FFNSs model’s evaluation of the persistence influence of news shocks on monthly visitor arrivals in Malaysia. The GJRGARCH-FFNSs model is the best model for Malaysian tourism demand volatility forecasting accuracy. Furthermore, KLCI and Gold Price have the most substantial impact on the number of tourists to Malaysia. In addition, it should be emphasised that the methodological framework utilised in this study can be a useful tool for creating and forecasting the performance of symmetry and asymmetry impacts on tourism demand volatility.

Tourism forecasting has garnered considerable interest. However, integrating tourism forecasting with volatility is significantly less typical. This study investigates the performance of both the single models and their combinations for forecasting the volatility of tourism demand. The seasonal autoregressive integrated moving average (SARIMA) model is used to construct the mean equation, and three single models, namely the generalized autoregressive conditional heteroscedasticity (GARCH) family models, the error-trend-seasonal exponential smoothing (ETS-ES) model, and the innovative smooth transition exponential smoothing (STES) model, are employed to estimate the volatility of monthly tourist arrivals into Malaysia. This study also assesses the accuracy of forecasts using simple average (SA), minimum variance (MV), and novel smooth transition (ST). STES performs the best of the single models for forecasting the out-of-sample of tourism demand volatility, followed closely by ETS-ES. In contrast, the ST combining method surpasses SA and MV. Interestingly, forecast combining methods do not always outperform the best single model, but they consistently outperform the worst single model. The MCS and DM tests confirm the aforementioned findings. This article merits consideration for future forecasting research on tourism demand volatility.

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